Multi-View Guided Multi-View Stereo

• URL: http://arxiv.org/abs/2210.11467v1
• Date: Thu, 20 Oct 2022 17:59:18 GMT
• Title: Multi-View Guided Multi-View Stereo
• Authors: Matteo Poggi, Andrea Conti, Stefano Mattoccia
• Abstract summary: This paper introduces a novel deep framework for dense 3D reconstruction from multiple image frames. Given a deep multi-view stereo network, our framework uses sparse depth hints to guide the neural network. We evaluate our Multi-View Guided framework within a variety of state-of-the-art deep multi-view stereo networks.
• Score: 39.116228971420874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a novel deep framework for dense 3D reconstruction from multiple image frames, leveraging a sparse set of depth measurements gathered jointly with image acquisition. Given a deep multi-view stereo network, our framework uses sparse depth hints to guide the neural network by modulating the plane-sweep cost volume built during the forward step, enabling us to infer constantly much more accurate depth maps. Moreover, since multiple viewpoints can provide additional depth measurements, we propose a multi-view guidance strategy that increases the density of the sparse points used to guide the network, thus leading to even more accurate results. We evaluate our Multi-View Guided framework within a variety of state-of-the-art deep multi-view stereo networks, demonstrating its effectiveness at improving the results achieved by each of them on BlendedMVG and DTU datasets.

Related papers

• Pixel-Aligned Multi-View Generation with Depth Guided Decoder [86.1813201212539]
  We propose a novel method for pixel-level image-to-multi-view generation. Unlike prior work, we incorporate attention layers across multi-view images in the VAE decoder of a latent video diffusion model. Our model enables better pixel alignment across multi-view images.
  arXiv  Detail & Related papers  (2024-08-26T04:56:41Z)
• SimpleMapping: Real-Time Visual-Inertial Dense Mapping with Deep Multi-View Stereo [13.535871843518953]
  We present a real-time visual-inertial dense mapping method with high quality using only monocular images and IMU readings. We propose a sparse point aided stereo neural network (SPA-MVSNet) that can effectively leverage the informative but noisy sparse points from the VIO system. Our proposed dense mapping system achieves a 39.7% improvement in F-score over existing systems when evaluated on the challenging scenarios of the EuRoC dataset.
  arXiv  Detail & Related papers  (2023-06-14T17:28:45Z)
• 3DVNet: Multi-View Depth Prediction and Volumetric Refinement [68.68537312256144]
  3DVNet is a novel multi-view stereo (MVS) depth-prediction method. Our key idea is the use of a 3D scene-modeling network that iteratively updates a set of coarse depth predictions. We show that our method exceeds state-of-the-art accuracy in both depth prediction and 3D reconstruction metrics.
  arXiv  Detail & Related papers  (2021-12-01T00:52:42Z)
• TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo [55.30992853477754]
  We present TANDEM, a real-time monocular tracking and dense framework. For pose estimation, TANDEM performs photometric bundle adjustment based on a sliding window of alignments. TANDEM shows state-of-the-art real-time 3D reconstruction performance.
  arXiv  Detail & Related papers  (2021-11-14T19:01:02Z)
• VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction [71.83308989022635]
  In this paper, we advocate that replicating the traditional two stages framework with deep neural networks improves both the interpretability and the accuracy of the results. Our network operates in two steps: 1) the local computation of the local depth maps with a deep MVS technique, and, 2) the depth maps and images' features fusion to build a single TSDF volume. In order to improve the matching performance between images acquired from very different viewpoints, we introduce a rotation-invariant 3D convolution kernel called PosedConv.
  arXiv  Detail & Related papers  (2021-08-19T11:33:58Z)
• OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems [88.41004332322788]
  We present an omnidirectional localization and dense mapping system for a wide-baseline multiview stereo setup with ultra-wide field-of-view (FOV) fisheye cameras. For more practical and accurate reconstruction, we first introduce improved and light-weighted deep neural networks for the omnidirectional depth estimation. We integrate our omnidirectional depth estimates into the visual odometry (VO) and add a loop closing module for global consistency.
  arXiv  Detail & Related papers  (2020-03-18T05:52:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.